Two-dimensional data such as data which is represented in “flat” tables or spreadsheets is often converted into graphical visualizations such as pie charts, bar graphs, and the like. Such two-dimensional data can be described as having “meta-data” at least in the form of the row names and the column names. The graphical visualizations can be presented using colors and line styles (dashed, dotted, etc.) to make the data more easily discerned by a viewer. Some spreadsheet program have graphing “wizards” which help the user configure a particular chart type, but they generally do not assist in picking the chart type which conveys the information that the user wants to communicate about the data.
It turns out that there are two reasons to chart data. The first is to illustrate some property or relationship of the data that is already known about the data in order to assist a presenter in showing the property, relationship or pattern to someone else. The second reason can be to explore and investigate the data, when patterns, relationships, correlations and properties are not known but may be suspected.
When the data is multi-dimensional above two-dimensions, it becomes exponentially more difficult to graph or visualize because human brains are particularly limited in their abilities to comprehend multi-dimensional data. For example, three-dimensional data can be illustrated in rotated and tilted “perspective views”, with three axes set to multiples of 30° or 60°. All three-dimensional data properties do not lend themselves to these types of representations using three apparently orthogonal axes which are actually reflected onto a two-dimensional medium such as paper or a flat computer screen.
When the known property of the data is complex, or when the data is multi-dimensional and the properties are unknown, then choosing the “best” visualization format can be something of a black art. For example, “Chart Tamer”™ by Bonavista is a plug-in helper program for the Microsoft Excel spreadsheet program which adds a button to the Excel menu bar. When a user wishes to create a data visualization or chart, he or she clicks on the Chart Tamer button and is provided a pop-up menu which includes a list of checkboxes in which the user indicates the data relationship (or property) that he or she wishes to illustrate or investigate, including value comparison, time series, part-to-whole ranking, correlation, distribution (multiple or single). This, however, requires a certain level of expertise on the user's part to even know what these “relationships” are and to suspect that such a relationship exists in the data. For data which is relatively unknown in its characteristics, or for relatively novice users, such a menu can fall short. Other software programs, web-based services and helper plug-ins provide similar menus of choices from which the user must select, such as iCharts™, Flot™, Raphael™, Modest Maps™, Leaflet™, WolframAlpha™, Visual.ly™, jQuery Visualize™, jqPlot™, IBM's ManyEyes™ (167,562 different visualization types and growing), Google Charts, etc.
Separately, there are texts books and online advice columns which provide users with example rules of thumb and anecdotal examples of how to pick the best data visualization for a particular data set or to show a particular characteristic of the data. For example, one such online resource (Chandoo<dot> org) lists the six common purposes for visualizing data as to compare, to show a distribution, to explain parts of a whole, to show a trend over time, to find deviations, and to understand a relationship. This correlates well and nearly one-to-one with the options provided in the Chart Tamer™ plug-in for Excel previously discussed, and shares the same requirement that the user understand the data and the visualization impact.
Both the Chandoo advice and the Chart Tamer helper program then map the user's choice (e.g. time series, parts of to the whole, distribution, etc.) to one or more actual chart types, such as:                (a) to compare two sets of data, use bar charts, column charts, scattergrams, pie charts, line charts or data tables;        (b) to illustrate distribution, use column charts, scattergrams, line charts or box plots;        (c) to show how parts of the data set contribute to the whole data set, use bar charts, column charts, pie charts, line charts or data tables; etc.        
From this short list of mapping purposes or reasons for visualizing data to possible chart types or chart formats, there is apparent aliasing in the mapping in that the chart options for comparing two sets of data are essentially the same as the chart options for parts-of-the-whole demonstrations. This may be because parts-of-the-whole illustrations are actually a species of comparing two data sets, wherein one data set is just a subset (e.g. part) of the whole set, e.g. there is 100% overlap between first set (the part) with the second set (the whole).
Such a situation, then, produces a quandary for a novice user, or for a user investigating a data set containing unknown or unidentified characteristics.